Lazy evaluation is a technique where the evaluation of an expression is deferred until its value is actually needed. This can be particularly beneficial when working with large datasets in Spark, as it allows for more efficient processing and reduced memory usage. By deferring the evaluation of certain operations, Spark can optimize the execution plan and avoid unnecessary computation. In the context of Pandas UDFs, lazy evaluation can be implemented by using iterators to process the data in chunks, rather than loading the entire dataset into memory at once. This allows for more efficient handling of large datasets, as only the necessary portion of the data is loaded and processed at any given time.